Abstract: Aiming at the problems of excessive model parameters and reconstruction distortion in existing single-image super-reso-lution convolutional neural networks,a lightweight single-image super-resolution network model based on dynamic pyramid structure and subspace attention module is proposed.First,the network body with dynamic multi-scale pyramid feature combination module consists of dynamic convolution and pyramid grouping convolution.Dynamic convolution can adaptively perform different convolution operations for different images,so as to extract different features for different images.Pyramid grouping convolution not only can better extract multi-scale features,but also can effectively reduce the number of parameters of the network model.Finally,a subspace attention module is used at the end of the network model to divide the channel space of images into multiple subspaces and learn different attention maps for each subspace,which not only can better capture the cross-channel rela-ted information of images,but also allows for effective fusion of image feature information of each subspace.Compared with the existing mainstream algorithms,the proposed method not only has a smaller number of model parameters,but also the reconstructed super-resolution images can achieve better performance in terms of visual effects and quantitative analysis.

Key words: Super-resolution, Lightweight, Dynamic convolution, Pyramid grouping convolution, Subspace attention block